Bibliographic record
Abstract
First let's sort out some historical and methodological issues. In a review of Raymond Tallis's Enemies of Hope a few years ago, the critic Robert Grant expressed what is now a familiar kind of historical assessment. About two nineteenth-century progenitors of contemporary theoretical discourses, he wrote: And it must be said that, ethically speaking … Marx and Nietzsche, did more than a little respectively to clear the ground for the Communist and Nazi atrocities to come. ( TLS , 14 Nov. 1997, 4) Grant explains that late twentieth-century theorists like Foucault, Derrida, and the rest of the usual suspects have inherited what he takes to be the moral nihilism of Marx and Nietzsche. Here is the familiar rhetoric which liberal and neoconservative ideologues share. The argument asserts that a post-structuralist literary critic, for example, as a byproduct of her work, strips human beings of their moral dimension and aids and abets their dehumanization, leading to pessimism, cynicism, and, no doubt, the Rwandan genocide. I suppose it is easier to blame a post-structuralist reading of Moby Dick , via Nietzsche's Thus Spoke Zarathustra , for the killing fields of the twentieth century, than get involved in the messy business of identifying the real culprits and causes. I find it difficult to imagine why others, like Adam Smith, Thomas Malthus, and Jeremy Bentham for example, have not been included on Grant's blacklist.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".